Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
Signal and System01:26

Signal and System

A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional signals...
Assembly of Signaling Complexes01:30

Assembly of Signaling Complexes

Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
Interaction domains recognize exposed features of their binding partners containing post-translationally modified sequences,...
Signal Flow Graphs01:18

Signal Flow Graphs

Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
Types of Signaling Molecules01:32

Types of Signaling Molecules

In multicellular organisms, many molecules transmit signals between cells to pass information. These signals vary in complexity and include small peptides, nucleotides, steroids, fatty acid derivatives, and dissolved gases such as nitric oxide. Some signaling molecules diffuse through the plasma membrane to act locally between neighboring cells or travel long distances. Others remain attached to the cell surface, transmitting information to other cells only when they make contact. In some...
Types of Signaling Molecules01:32

Types of Signaling Molecules

In multicellular organisms, many molecules transmit signals between cells to pass information. These signals vary in complexity and include small peptides, nucleotides, steroids, fatty acid derivatives, and dissolved gases such as nitric oxide. Some signaling molecules diffuse through the plasma membrane to act locally between neighboring cells or travel long distances. Others remain attached to the cell surface, transmitting information to other cells only when they make contact. In some...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Large language models and emergence: a complex systems perspective.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same author

Collective cooperative intelligence.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Constructing stability: optimal learning in noisy ecological niches.

Proceedings. Biological sciences·2024
Same author

Outsourcing Control Requires Control Complexity.

Artificial life·2024
Same author

Symmetry-simplicity, broken symmetry-complexity.

Interface focus·2023
Same author

The debate over understanding in AI's large language models.

Proceedings of the National Academy of Sciences of the United States of America·2023

Related Experiment Videos

Robustness and complexity co-constructed in multimodal signalling networks.

Nihat Ay1, Jessica Flack, David C Krakauer

  • 1Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|January 27, 2007
PubMed
Summary

Multimodal animal signals enhance communication robustness by using correlated channels. This robust overdesign principle explains how redundancy evolves, enabling novel signal specialization in complex communication systems.

Related Experiment Videos

Area of Science:

  • Animal Communication
  • Evolutionary Biology
  • Computational Neuroscience

Background:

  • Animal signals often utilize multiple channels and modalities.
  • Existing explanations for multimodality include information transfer and signal robustness.

Purpose of the Study:

  • To analytically investigate explanations for multimodal signaling in animal communication.
  • To determine if multimodality enhances information transfer or signal robustness.

Main Methods:

  • Analytical modeling using simple feed-forward neural networks.
  • Mathematical analysis of signal robustness and information transfer in multichannel versus multimodal systems.

Main Results:

  • Multimodal signals are more effective than multichannel signals at solving the robustness problem.
  • Robustness in multimodal signals leads to correlated channels and complex associative networks (robust overdesign).

Conclusions:

  • The principle of robust overdesign explains the evolution of multimodal signaling.
  • Redundancy fostered by robustness allows for the specialization of components into novel signals, driving combinatorial signaling systems.